This document summarizes a research paper that proposes a method for early prediction of arthritis using thermal image processing and neural networks. The method involves taking thermal images of affected joints, selecting the region of interest, calculating temperature based on pixel color, and using a backpropagation neural network to predict arthritis based on the measured temperature. The paper outlines related work on arthritis detection using techniques like thermal imaging, image processing, and machine learning. It then describes the proposed methodology which includes thermal image processing to measure joint temperature and a backpropagation neural network to predict arthritis. Preliminary results show the potential of this method to predict arthritis at an early stage by analyzing temperature changes in thermal images of affected joints.

1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1170
Arthritis Prediction using Thermal Images and Neural Network
Swati Wasnik 1, Vrishali Fulzele2, Ishika Vora3, Pankaj Khoabragade4, Mrunal Raut5, Md
Waseem Khanooni6
1Swati Wasnik, Dept. Of Electronics and Telecommunication Engineering, SBJITMR, Maharashtra, India
2Vrishali Fulzele, Dept. Of Electronics and Telecommunication Engineering, SBJITMR, Maharashtra, India
3Ishika Vora, Dept. Of Electronics and Telecommunication Engineering, SBJITMR, Maharashtra, India
4Pankaj Khobragade, Dept. Of Electronics and Telecommunication Engineering, SBJITMR, Maharashtra, India
5 Mrunal Raut, Dept. Of Electronics and Telecommunication Engineering, SBJITMR, Maharashtra, India
6Md Waseem Khanooni, Dept. Of Electronics and Telecommunication Engineering, SBJITMR, Maharashtra, India
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Abstract - Arthritis is the swelling and tenderness of one or
more of the joints. Joint pain and stiffness are the main
symptoms which worsen with age. It targets all ages but it is
more common in women. To overcome this problem we need
some intelligent device that can help us to give early
prediction of arthritis disease. In this paper, artificial neural
networks and thermal image processing using MATLAB
(Matrix Laboratory) used forearlyprediction. Somestagesare
present in this system. First, we will load the thermal image in
the GUI created using MATLAB for processing. Then, Select the
affected region. The system will read the pixels of selected
region and calculate the temperaturebasedoncolorofpixelin
thermal images. When inflammation occurs, chemicals from
the body’s white blood cells (WBC) are released into theblood.
This release of chemicals increases the blood flow to the area
of injury or infection with increase in the temperature, and
may result in redness and warmth. On the basis of
temperature, the early prediction will be done by using
Backpropagation algorithm.
Key Words: Arthritis, backpropagation algorithm,
inflammation, white blood cells, artificial neural
networks
1. INTRODUCTION
Arthritis means inflammation of joints. It can affectchildren,
as well as people in the prime of their lives. Arthritis is a
damn serious disease, it cause more death then Melanoma
(Skin cancer),Asthma, HIV aids,etc. It is commonly seen in
elder population above age 40 mostly in females. Arthritis
can be controlled but if it is predicted at early stage then it
can be cured as well. Our project is based on the same
concept of early prediction to reduce death rate of arthritis
patients.
Thermal image processing using MATLAB plays a majorrole
in calculation of temperature oftheaffectedarea.Thesystem
will detect, display and record thermal patterns and
temperature values across a given surface. A thermal image
converts the invisible infrared imagesintoa monochromeor
multicolored image. Once captured, thermal images and
associated temperatures can be stored for future reference
and analysis.
Fig -1: Stages of arthritis patients
2. LITERATURE REVIEWS
RozinaNaz [1],this research paper developed a new
intelligent system for the identification and prediction of
rheumatoid arthritis. They used thermal image processing
techniques and neural network. It uses backpropagation
algorithm.
Swati Bhisikar [2] paper basedonconceptarthritisdiseaseis
detected by radiograph.They focused on image processing
techniques using MATLAB to analyze Joint Space
Width(JSW).They have used Active Shape Model(ASM) for
extracting information through images. Then, Local Linear
Mapping is used to detect JSW.
Kento Morita [3] paper says that the estimation of arthritis
can be done early by using progression of RA.The modified
Total shape(mTS) score was used to predict.Butittakes long
time.So this paper focused to make automatic mTS score
assessments on hand using score vector machine.
Lakshmi TV [4], According to this paper the Rheumatoid
arthritis is a genetic disorder. So, researchers predicted the
RA by genomic sequence analysis. They have used signal
processing algorithm and cross correlation.

2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1171
3. METHODOLOGY
The purpose of our software is to detect the arthritis of
people who are having chances of suffering from arthritis or
any bone diseases. By prediction, the person can starttaking
care of it and the precautions can be taken at the early stage.
3.1 THERMAL IMAGE PROCESSING
X-Ray and MRI are expensive methods to detect arthritis
disease whereas thermal image processing allows us toheat
as a factor where different colors represent different range
of temperatures. It contains red, green, lime, yellow, blue,
cyan and magenta. The red or yellow-orange hue represents
warmer area. Blue or purple represent cooler area. Thermal
image processing has the ability to performinspectionwhile
temperatures are undertheload.Thermal inspection quickly
locates affected regions and calculates the temperature by
calculating the average red color pixels to predict the
chances of arthritis which can be treated at early age.
3.2 NEURAL NETWORK
A neural network is composed of connection nodes like
brain is composed of nodes. It uses connection of weights.
Input layer: It defines number of input given to the neural
network.
Hidden layers: It is having number of layers with arbitrary
number of neurons.
Output layers: The number of output values corresponds
to the output layer.
Fig -2: Layers in Neural Network
Here, the project consist 4 inputs, 1 output and the output
will be based on 10 best records.
3.2.1 BACKPROPAGATION ALGORITHM
It is a supervised learning algorithm. When designing a
neural network, we initialize weights with some random
values or any variable in the beginning. But our model
output will have huge error value. So, to reducetheerror, we
need to somehow explain the model to change the weights
such that error should be minimum. By using a technique
called the delta rule orgradientdescentthebackpropagation
algorithm looks for the minimum value of the error function
in the weight space.
3.4 IMPLEMENTATION AND RESULT:
Fig -3: Flowchart of work
In this project, we need thermal image to load andprocessit.
Then, select region of joints. Here, we have taken four joints
of hands i.e. Distal interphalangeal joint (DIP),
Metacarpophalangeal joint (MCP), Proximal interphalangeal
joint (PIP) and wrist. Select the region of the particular joint
one by one and click on the button of the respective joint.
The arthritis can be happen in any one ofthejointornumber
of joints. So, if any one of the joints having chances of
arthritis then the result will bechancesofarthritisotherwise
it would be normal case. The thermal range is 29˚C-32˚C for
arthritis patients.. The output is shown in fig 2.
Fig -3: Output of work

3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1172
4. CONCLUSIONS
As there is intense need of medication for the diseases that
can’t be identified at the early stages and could lead to
severe health issues. One of the diseases is arthritis. Our
project is based on the same concept to provide a new
intelligent system to hospitals. It is a prediction system that
helps people to predict the chances of arthritisatearlystage.
It will help them to protect themselves by taking early steps
for prevention.
We used MATLAB R2013a in our project for image
processing andbackpropagationalgorithmfortrainingof the
neural network .In future; it can be also used to detect other
diseases where temperature of the affected region is
different with respect to whole body.
REFERENCES
[1] Rozina Naz, Moshtashim Ahmad, Mr.Manishkarandikar,
“Arthritis prediction by thermal Image processing and
neural network”, ISQR,August-2015.
[2] Mrs. Swati Bhisikar and Dr.Sujata Nikale, ”Automatic
Analysis of Rheumatoid Arthritis based on Statistical
features “,IEEE,2016
[3] Kento Morita, Atsukitashita, Manabunif, syojikobashi,
“Computer aided diagnosis system for Rheumatoid arthritis
using machine learning ”, IEEE,2017
[4] Lakshmi TV, Dr K.B Ramesh ,Dr. VidyaNiranjan,
Aishwarya Shetty,Monica N,Aishwarya Rao,”Design of a
state-machine based genomic simulatoranddevelopment of
a system for prediction of RA using signal processing
”,IEEE,2017
[5] Sudhir Rathore and Prof. S.V. Bhalerao,”Designing of
Neuro-Fuzzy BasedThermal ImagingSystemonMATLABfor
Detection of Rheumatoid Arthritis”,International Journal of
Engineering Research and General Science,March-April,
2015
[6] Abhishek Ghosh, Sayanatni Bera,Geetashree Kundu,
SudiptaBhadra, AnnindyaJyoti Pal,”A Survey on Thermal
Image Processing Using GA, ANN and Fuzzy C-Means
Algorithm”,International Journal of Innovative Research in
Science, Engineering and Technology,June 2016
[7] Kadir Tok and Ismail Saritas, ”The Usage of Artificial
Neural Networks Method in the Diagnosis of Rheumatoid
Arthritis”,International Journal of Intelligent Systems and
Applications in Engineering, 2016
[8]Nizami Mohiyuddin,Pradeepkumar Dhage,Krishna K.
Warhade,”Rheumatoid Arthritis Detection Using Thermal
ImagingandFuzzy-C-Meansalgorithm”,International Journal
of Computer & Mathematical Sciences,October 2014